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Recently computer systems' call sequences are considered as a data source, this paper expounds how to use Hidden Markov Models (HMM) for software behavior recognition and trend prediction. Due to that HMM is sensitive to initial parameters, especially sensitive to B-parameter which makes model fall into a local optimum in training, this paper proposes using Genetic Algorithm (GA) approach to optimize...
This paper introduces a new back-end classifier for a speech recognition system that is based on artificial life (ALife). The ALife species being used for classification purposes are called wains, which were developed using the Créatúr framework. The speech recognition task used in the evaluation of the new classifier is that of isolated digit recognition. Performance of the proposed back-end classifier...
This paper proposes a system for text-independent writer identification based on Arabic handwriting using only 21 features. Gaussian Mixture Models (GMMs) are used as the core of the system. GMMs provide a powerful representation of the distribution of features extracted using a fixed-length sliding window from the text lines and words of a writer. For each writer a GMM is built and trained using...
We propose a recognition method based on statistics through analysis the grammatical and semantic characteristics of the Chinese organization name. This recognition method includes three elements: frequency, part of speech, word length. We use the data in mature collection as training data; separately calculate a candidate organization name's word frequency, part of speech and word length of the contribution...
A method of generating multi-document summary was proposed based on concept maps and Hidden Markov Model. Handling the original text file, extract concept based on improved Hidden Markov Model and generate concept map, finally calculate the importance and related degree of sentences according to concept map, and then generate semantic summary. The experiment result shows that the summary generated...
Neurobiological research has uncovered the existence of cortical neurons in various animal species tuned to particular spectro-temporal modulations (STM) in the auditory stimulus. Other findings indicate that temporal statistics of the resulting neural spike trains may encode the underlying content of species-specific communication calls. With this motivation, we present an alternative approach to...
In this paper, the Markov Family Models, a kind of statistical Models was firstly introduced. Under the assumption that the probability of a word depends both on its own tag and previous word, but its own tag and previous word are independent if the word is known, we simplify the Markov Family Model and use for part-of-speech tagging successfully. Experimental results show that this part-of-speech...
In HMM-based speech synthesis, we usually use complex, context dependent models to characterize prosodically and linguistically rich speech units. It is therefore difficult to prepare training data which can cover all combinatorial possibilities of contexts. A common approach to cope with this insufficient training data problem is to build a clustered tree via the MDL criterion. However, an MDL-based...
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